


"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


UpperCAmelCase				=						logging.get_logger(__name__)




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					['''input_features''', '''is_longer''']





   def __init__(							self      ,   SCREAMING_SNAKE_CASE=64      ,   SCREAMING_SNAKE_CASE=4_8000      ,   SCREAMING_SNAKE_CASE=480      ,   SCREAMING_SNAKE_CASE=10      ,   SCREAMING_SNAKE_CASE=1024      ,   SCREAMING_SNAKE_CASE=0.0      ,   SCREAMING_SNAKE_CASE=False      ,   SCREAMING_SNAKE_CASE = 0      ,   SCREAMING_SNAKE_CASE = 1_4000      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = "fusion"      ,   SCREAMING_SNAKE_CASE = "repeatpad"      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  Dict:
      super().__init__(
          feature_size=SCREAMING_SNAKE_CASE      ,   sampling_rate=SCREAMING_SNAKE_CASE      ,   padding_value=SCREAMING_SNAKE_CASE      ,   return_attention_mask=SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE      ,   )
      _lowerCamelCase	:					List[Any]   =		top_db
      _lowerCamelCase	:					Dict   =		truncation
      _lowerCamelCase	:					str   =		padding
      _lowerCamelCase	:					Any   =		fft_window_size
      _lowerCamelCase	:					Dict   =		(fft_window_size >> 1) + 1
      _lowerCamelCase	:					Optional[Any]   =		hop_length
      _lowerCamelCase	:					Any   =		max_length_s
      _lowerCamelCase	:					List[str]   =		max_length_s * sampling_rate
      _lowerCamelCase	:					List[str]   =		sampling_rate
      _lowerCamelCase	:					List[Any]   =		frequency_min
      _lowerCamelCase	:					str   =		frequency_max
      _lowerCamelCase	:					List[Any]   =		mel_filter_bank(
          num_frequency_bins=self.nb_frequency_bins      ,   num_mel_filters=SCREAMING_SNAKE_CASE      ,   min_frequency=SCREAMING_SNAKE_CASE      ,   max_frequency=SCREAMING_SNAKE_CASE      ,   sampling_rate=SCREAMING_SNAKE_CASE      ,   norm=SCREAMING_SNAKE_CASE      ,   mel_scale="""htk"""      ,   )
      _lowerCamelCase	:					Union[str, Any]   =		mel_filter_bank(
          num_frequency_bins=self.nb_frequency_bins      ,   num_mel_filters=SCREAMING_SNAKE_CASE      ,   min_frequency=SCREAMING_SNAKE_CASE      ,   max_frequency=SCREAMING_SNAKE_CASE      ,   sampling_rate=SCREAMING_SNAKE_CASE      ,   norm="""slaney"""      ,   mel_scale="""slaney"""      ,   )





   def 		UpperCamelCase_  (							self)  ->  Dict[str, Any]:
      _lowerCamelCase	:					Optional[int]   =		copy.deepcopy(self.__dict__)
      _lowerCamelCase	:					Optional[Any]   =		self.__class__.__name__
      if "mel_filters" in output:
         del output["mel_filters"]
      if "mel_filters_slaney" in output:
         del output["mel_filters_slaney"]
      return output





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None)  ->  np.ndarray:
      _lowerCamelCase	:					Optional[Any]   =		spectrogram(
          SCREAMING_SNAKE_CASE      ,   window_function(self.fft_window_size      ,   """hann""")      ,   frame_length=self.fft_window_size      ,   hop_length=self.hop_length      ,   power=2.0      ,   mel_filters=SCREAMING_SNAKE_CASE      ,   log_mel="""dB"""      ,   )
      return log_mel_spectrogram.T





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Optional[int]:
      _lowerCamelCase	:					int   =		np.array_split(list(range(0      ,   total_frames - chunk_frames + 1))      ,   3)
      if len(ranges[1]) == 0:
         # if the audio is too short, we just use the first chunk
         _lowerCamelCase	:					List[Any]   =		[0]
      if len(ranges[2]) == 0:
         # if the audio is too short, we just use the first chunk
         _lowerCamelCase	:					List[Any]   =		[0]
      # randomly choose index for each part
      _lowerCamelCase	:					List[str]   =		np.random.choice(ranges[0])
      _lowerCamelCase	:					Union[str, Any]   =		np.random.choice(ranges[1])
      _lowerCamelCase	:					Any   =		np.random.choice(ranges[2])

      _lowerCamelCase	:					Union[str, Any]   =		mel[idx_front : idx_front + chunk_frames, :]
      _lowerCamelCase	:					Union[str, Any]   =		mel[idx_middle : idx_middle + chunk_frames, :]
      _lowerCamelCase	:					Dict   =		mel[idx_back : idx_back + chunk_frames, :]

      _lowerCamelCase	:					Optional[int]   =		torch.tensor(mel[None, None, :])
      _lowerCamelCase	:					Tuple   =		torch.nn.functional.interpolate(
          SCREAMING_SNAKE_CASE      ,   size=[chunk_frames, 64]      ,   mode="""bilinear"""      ,   align_corners=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Dict   =		mel_shrink[0][0].numpy()
      _lowerCamelCase	:					List[str]   =		np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]      ,   axis=0)
      return mel_fusion





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  np.array:
      if waveform.shape[0] > max_length:
         if truncation == "rand_trunc":
            _lowerCamelCase	:					Dict   =		True
            # random crop to max_length (for compatibility) -> this should be handled by self.pad
            _lowerCamelCase	:					List[str]   =		len(SCREAMING_SNAKE_CASE) - max_length
            _lowerCamelCase	:					str   =		np.random.randint(0      ,   overflow + 1)
            _lowerCamelCase	:					Any   =		waveform[idx : idx + max_length]
            _lowerCamelCase	:					Tuple   =		self._np_extract_fbank_features(SCREAMING_SNAKE_CASE      ,   self.mel_filters_slaney)[None, :]
         elif truncation == "fusion":
            _lowerCamelCase	:					str   =		self._np_extract_fbank_features(SCREAMING_SNAKE_CASE      ,   self.mel_filters)
            _lowerCamelCase	:					List[str]   =		max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
            _lowerCamelCase	:					List[str]   =		mel.shape[0]
            if chunk_frames == total_frames:
               # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
               # In this case, we just use the whole audio.
               _lowerCamelCase	:					Dict   =		np.stack([mel, mel, mel, mel]      ,   axis=0)
               _lowerCamelCase	:					List[Any]   =		False
            else:
               _lowerCamelCase	:					Tuple   =		self._random_mel_fusion(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)
               _lowerCamelCase	:					Any   =		True
         else:
            raise NotImplementedError(F'data_truncating {truncation} not implemented')

      else:
         _lowerCamelCase	:					Union[str, Any]   =		False
         # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
         if waveform.shape[0] < max_length:
            if padding == "repeat":
               _lowerCamelCase	:					Tuple   =		int(max_length / len(SCREAMING_SNAKE_CASE))
               _lowerCamelCase	:					Union[str, Any]   =		np.stack(np.tile(SCREAMING_SNAKE_CASE      ,   n_repeat + 1))[:max_length]
            if padding == "repeatpad":
               _lowerCamelCase	:					List[Any]   =		int(max_length / len(SCREAMING_SNAKE_CASE))
               _lowerCamelCase	:					str   =		np.stack(np.tile(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE))
            _lowerCamelCase	:					str   =		np.pad(SCREAMING_SNAKE_CASE      ,   (0, max_length - waveform.shape[0])      ,   mode="""constant"""      ,   constant_values=0)

         if truncation == "fusion":
            _lowerCamelCase	:					Optional[Any]   =		self._np_extract_fbank_features(SCREAMING_SNAKE_CASE      ,   self.mel_filters)
            _lowerCamelCase	:					Optional[Any]   =		np.stack([input_mel, input_mel, input_mel, input_mel]      ,   axis=0)
         else:
            _lowerCamelCase	:					Tuple   =		self._np_extract_fbank_features(SCREAMING_SNAKE_CASE      ,   self.mel_filters_slaney)[None, :]

      return input_mel, longer





   def __call__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = None      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  BatchFeature:
      _lowerCamelCase	:					Tuple   =		truncation if truncation is not None else self.truncation
      _lowerCamelCase	:					int   =		padding if padding else self.padding

      if sampling_rate is not None:
         if sampling_rate != self.sampling_rate:
            raise ValueError(
                F'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
                F' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
                F' was sampled with {self.sampling_rate} and not {sampling_rate}.')
      else:
         logger.warning(
             """It is strongly recommended to pass the `sampling_rate` argument to this function. """
             """Failing to do so can result in silent errors that might be hard to debug.""")

      _lowerCamelCase	:					Dict   =		isinstance(SCREAMING_SNAKE_CASE      ,   np.ndarray) and len(raw_speech.shape) > 1
      if is_batched_numpy and len(raw_speech.shape) > 2:
         raise ValueError(F'Only mono-channel audio is supported for input to {self}')
      _lowerCamelCase	:					Any   =		is_batched_numpy or (
          isinstance(SCREAMING_SNAKE_CASE      ,   (list, tuple)) and (isinstance(raw_speech[0]      ,   (np.ndarray, tuple, list)))
      )

      if is_batched:
         _lowerCamelCase	:					Any   =		[np.asarray(SCREAMING_SNAKE_CASE      ,   dtype=np.floataa) for speech in raw_speech]
      elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE      ,   np.ndarray):
         _lowerCamelCase	:					int   =		np.asarray(SCREAMING_SNAKE_CASE      ,   dtype=np.floataa)
      elif isinstance(SCREAMING_SNAKE_CASE      ,   np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
         _lowerCamelCase	:					str   =		raw_speech.astype(np.floataa)

      # always return batch
      if not is_batched:
         _lowerCamelCase	:					Dict   =		[np.asarray(SCREAMING_SNAKE_CASE)]

      # convert to mel spectrogram, truncate and pad if needed.
      _lowerCamelCase	:					int   =		[
          self._get_input_mel(SCREAMING_SNAKE_CASE      ,   max_length if max_length else self.nb_max_samples      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)
          for waveform in raw_speech
      ]

      _lowerCamelCase	:					Dict   =		[]
      _lowerCamelCase	:					Optional[int]   =		[]
      for mel, longer in padded_inputs:
         input_mel.append(SCREAMING_SNAKE_CASE)
         is_longer.append(SCREAMING_SNAKE_CASE)

      if truncation == "fusion" and sum(SCREAMING_SNAKE_CASE) == 0:
         # if no audio is longer than 10s, then randomly select one audio to be longer
         _lowerCamelCase	:					Any   =		np.random.randint(0      ,   len(SCREAMING_SNAKE_CASE))
         _lowerCamelCase	:					List[Any]   =		True

      if isinstance(input_mel[0]      ,   SCREAMING_SNAKE_CASE):
         _lowerCamelCase	:					Any   =		[np.asarray(SCREAMING_SNAKE_CASE      ,   dtype=np.floataa) for feature in input_mel]

      # is_longer is a list of bool
      _lowerCamelCase	:					Union[str, Any]   =		[[longer] for longer in is_longer]

      _lowerCamelCase	:					Optional[int]   =		{"""input_features""": input_mel, """is_longer""": is_longer}
      _lowerCamelCase	:					Optional[int]   =		BatchFeature(SCREAMING_SNAKE_CASE)

      if return_tensors is not None:
         _lowerCamelCase	:					List[Any]   =		input_features.convert_to_tensors(SCREAMING_SNAKE_CASE)

      return input_features









'''simple docstring'''





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def __UpperCAmelCase   (					A					:			Optional[int]		)    ->   List[Any]:
			# This defines a "chinese character" as anything in the CJK Unicode block:
			#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
			#
			# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
			# despite its name. The modern Korean Hangul alphabet is a different block,
			# as is Japanese Hiragana and Katakana. Those alphabets are used to write
			# space-separated words, so they are not treated specially and handled
			# like the all of the other languages.
			if (
			    (cp >= 0X4E_00 and cp <= 0X9F_FF)
			    or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
			    or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
			    or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
			    or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
			    or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
			    or (cp >= 0XF9_00 and cp <= 0XFA_FF)
			    or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
			):  #
						return True

			return False
def __UpperCAmelCase   (					A					:			str		)    ->   Optional[Any]:
			# word like '180' or '身高' or '神'
			for char in word:
						UpperCAmelCase_      :       str          =       ord(A		)
						if not _is_chinese_char(A		):
									return 0
			return 1
def __UpperCAmelCase   (					A					:			List[str]		)    ->   Dict:
			UpperCAmelCase_      :       Optional[Any]          =       set()

			for token in tokens:
						UpperCAmelCase_      :       str          =       len(A		) > 1 and is_chinese(A		)
						if chinese_word:
									word_set.add(A		)
			UpperCAmelCase_      :       Optional[int]          =       list(A		)
			return word_list
def __UpperCAmelCase   (					A					:			List[str]			,       A					:			set()		)    ->   Optional[Any]:
			if not chinese_word_set:
						return bert_tokens
			UpperCAmelCase_      :       Dict          =       max([len(A		) for w in chinese_word_set]		)

			UpperCAmelCase_      :       List[str]          =       bert_tokens
			UpperCAmelCase_    ,      UpperCAmelCase_      :       List[Any]          =       0, len(A		)
			while start < end:
						UpperCAmelCase_      :       str          =       True
						if is_chinese(bert_word[start]		):
									UpperCAmelCase_      :       str          =       min(end - start			,       A		)
									for i in range(A			,       1			,       -1		):
												UpperCAmelCase_      :       Tuple          =       ''''''.join(bert_word[start : start + i]		)
												if whole_word in chinese_word_set:
															for j in range(start + 1			,       start + i		):
																		UpperCAmelCase_      :       Union[str, Any]          =       '''##''' + bert_word[j]
															UpperCAmelCase_      :       Any          =       start + i
															UpperCAmelCase_      :       Optional[int]          =       False
															break
						if single_word:
									start += 1
			return bert_word
def __UpperCAmelCase   (					A					:			List[str]			,       A					:			LTP			,       A					:			BertTokenizer		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       Optional[int]          =       []

			for i in range(0			,       len(A		)			,       1_0_0		):
						UpperCAmelCase_      :       int          =       ltp_tokenizer.pipeline(lines[i : i + 1_0_0]			,       tasks=['''cws''']		).cws
						UpperCAmelCase_      :       Any          =       [get_chinese_word(A		) for r in res]
						ltp_res.extend(A		)
			assert len(A		) == len(A		)

			UpperCAmelCase_      :       Tuple          =       []
			for i in range(0			,       len(A		)			,       1_0_0		):
						UpperCAmelCase_      :       Optional[int]          =       bert_tokenizer(lines[i : i + 1_0_0]			,       add_special_tokens=A			,       truncation=A			,       max_length=5_1_2		)
						bert_res.extend(res['''input_ids''']		)
			assert len(A		) == len(A		)

			UpperCAmelCase_      :       Any          =       []
			for input_ids, chinese_word in zip(A			,       A		):
						UpperCAmelCase_      :       Union[str, Any]          =       []
						for id in input_ids:
									UpperCAmelCase_      :       Union[str, Any]          =       bert_tokenizer._convert_id_to_token(A		)
									input_tokens.append(A		)
						UpperCAmelCase_      :       List[str]          =       add_sub_symbol(A			,       A		)
						UpperCAmelCase_      :       Any          =       []
						# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
						for i, token in enumerate(A		):
									if token[:2] == "##":
												UpperCAmelCase_      :       int          =       token[2:]
												# save chinese tokens' pos
												if len(A		) == 1 and _is_chinese_char(ord(A		)		):
															ref_id.append(A		)
						ref_ids.append(A		)

			assert len(A		) == len(A		)

			return ref_ids
def __UpperCAmelCase   (					A					:			List[Any]		)    ->   Tuple:
			# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
			# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
			with open(args.file_name			,       '''r'''			,       encoding='''utf-8'''		) as f:
						UpperCAmelCase_      :       Optional[Any]          =       f.readlines()
			UpperCAmelCase_      :       List[str]          =       [line.strip() for line in data if len(A		) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
			UpperCAmelCase_      :       Tuple          =       LTP(args.ltp		)  # faster in GPU device
			UpperCAmelCase_      :       Union[str, Any]          =       BertTokenizer.from_pretrained(args.bert		)

			UpperCAmelCase_      :       Optional[int]          =       prepare_ref(A			,       A			,       A		)

			with open(args.save_path			,       '''w'''			,       encoding='''utf-8'''		) as f:
						UpperCAmelCase_      :       str          =       [json.dumps(A		) + '''\n''' for ref in ref_ids]
						f.writelines(A		)


if __name__ == "__main__":
						_UpperCamelCase						:						Optional[Any]  =					argparse.ArgumentParser(description='prepare_chinese_ref')
						parser.add_argument(
						    '--file_name',
						    required=False,
						    type=str,
						    default='./resources/chinese-demo.txt',
						    help='file need process, same as training data in lm',
						)
						parser.add_argument(
						    '--ltp',
						    required=False,
						    type=str,
						    default='./resources/ltp',
						    help='resources for LTP tokenizer, usually a path',
						)
						parser.add_argument(
						    '--bert',
						    required=False,
						    type=str,
						    default='./resources/robert',
						    help='resources for Bert tokenizer',
						)
						parser.add_argument(
						    '--save_path',
						    required=False,
						    type=str,
						    default='./resources/ref.txt',
						    help='path to save res',
						)

						_UpperCamelCase						:						Any  =					parser.parse_args()
						main(args)


